Know How and Know What for Software Processes

  • Jan Kožusznik
  • Svatopluk Štolfa
  • Marie Duží
  • Michal Košinár
  • Martina Číhalová
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 230)


Formal specification of a software process, as well as its optimal design, is a fundamental landmark and tenet that any successful software company must follow. Recent trends can be characterized as a knowledge-base support of the software-process development, standardization and improvement. To this end we create semantic annotations (ontologies) of processes which should serve as a stable unifying core of the software-process development. However, when doing so, we meet the problem how to transform various forms of tacit, implicit knowledge into an explicit knowledge specification that is logically tractable and machine readable. In this paper we focus on the transformation of informal tacit knowledge about a software process (or any part of the process) to the formal knowledge specification that can be used for building machine readable knowledge bases. In particular, we aim at optimizing and improving software-process development using knowledge bases which are created to the purpose of a formal description of the software-process development.


Software process improvement Knowledge Rules Facts Knowledge base Software process 


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  1. 1.
    Humphrey, W.S.: A Discipline for Software Engineering. Addison-Wesley Professional, Reading (1995)Google Scholar
  2. 2.
    Thayer, R.H.: Software System Engineering: A Tutorial. Computer 35(4), 68–73 (2002), doi:10.1109/mc.2002.993773CrossRefGoogle Scholar
  3. 3.
    Makinen, T., Varkoi, T.: Assessment driven process modeling for software process improvement. In: International Conference on Management of Engineering & Technology, PICMET 2008, Portland, July 27-31, pp. 1570–1575 (2008) Google Scholar
  4. 4.
    Software Engineering Institute: CMMI staged-version 1.1. (2002) Google Scholar
  5. 5.
    Garg, P.K., Scacchi, W.: ISHYS: Designing an Intelligent Software Hypertext System. IEEE Expert: Intelligent Systems and Their Applications 4(3), 52–63 (1989)CrossRefGoogle Scholar
  6. 6.
    Mi, P., Scacchi, W.: A Knowledge-Based Environment for Modeling and Simulating Software Engineering Processes. IEEE Trans. on Knowl. and Data Eng. 2(3), 283–294 (1990), doi:10.1109/69.60792CrossRefGoogle Scholar
  7. 7.
    Mi, P., Scacchi, W.: A meta-model for formulating knowledge-based models of software development. Decis. Support. Syst. 17(4), 313–330 (1996),, doi:10.1016/0167-9236(96)00007-3CrossRefGoogle Scholar
  8. 8.
    Raffo, D.M.: Modeling software processes quantitatively and assessing the impact of potential process changes on process performance. Ph.D. thesis, Carnegie Mellon University (1996)Google Scholar
  9. 9.
    Madachy, R.J.: Software Process Dynamics, 2nd edn. Wiley-IEEE Press (2008)Google Scholar
  10. 10.
    Scacchi, W.: Experience with software process simulation and modeling. J. Syst. Softw. 46(2-3), 183–192 (1999)CrossRefGoogle Scholar
  11. 11.
    Peterson, J.L.: Petri Net Theory and the Modeling of Systems. Prentice Hall, Englewood Cliffs (1981)zbMATHGoogle Scholar
  12. 12.
    Mi, P., Scacchi, W.: Articulation: an integrated approach to the diagnosis, replanning, and rescheduling of software process failures. In: Proceedings of Eighth Knowledge-Based Software Engineering Conference, pp. 77–84 (1993)Google Scholar
  13. 13.
    Garg, P.K., Mi, P.W., Thuan, P., Scacchi, W., Thunquest, G.: The Smart Approach for Software Process Engineering. In: Proc. Int. Conf. Softw., pp. 341–350 (1994)Google Scholar
  14. 14.
    Workflow Management Coalition: Terminology & Glossary (1999) Google Scholar
  15. 15.
    Brachman, R., Levesque, H.: Knowledge Representation and Reasoning. Morgan Kaufmann, San Francisco (2004)zbMATHGoogle Scholar
  16. 16.
    Frydrych, T., Kohut, O., Košinár, M.: Transparent Intensional Logic in Knowledge Based Multiagent Systems. In: Sojka, P., Horák, A. (eds.) RASLAN 2008. Masaryk University, Brno (2008)Google Scholar
  17. 17.
    Ciprich, N., Duží, M., Košinár, M.: The TIL-Script Language. In: Kiyoki, Y., Tokuda, T., Jaakola, H., Chen, X., Yoshida, N. (eds.) Information Modelling and Knowledge Bases XX, pp. 166–179. IOS Press, Amsterdam (2009)Google Scholar
  18. 18.
    Ciprich, N., Duží, M., Košinár, M.: TIL-Script: Functional Programming Based on Transparent Intensional Logic. In: Sojka, P., Horák, A. (eds.) RASLAN 2007, pp. 37–42. Masaryk University, Brno (2007)Google Scholar
  19. 19.
    Noy, N.F., Sintek, M., Decker, S., Crubézy, M., Fergerson R.W., Musen M.A.: Creating Semantic Web Contents with Protégé-2000, vol. 16 (2001)Google Scholar
  20. 20.
    Kruchten, P.: The Rational Unified Process: An Introduction. Addison-Wesley Professional, Reading (2003)Google Scholar
  21. 21.
    Karkoška, T.: Vytvoření nástroje pro podporu tvorby ontologií v multi-agentním prostředí. Master thesis, VŠB-TUO, Ostrava, Czech Republic (2008) Google Scholar
  22. 22.
    Jezek, D., Vondrak, I.: HDA and resources modeling in business process. In: Baake, U.F., Herbst, J., VanLandeghem, R. (eds.) 11th European Concurrent Engineering Conference 2004 - Worldwide Partnerships and Mergers, pp. 27–29 (2004)Google Scholar
  23. 23.
    W3C: OWL 2 Web Ontology Language (2009),

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jan Kožusznik
    • 1
  • Svatopluk Štolfa
    • 1
  • Marie Duží
    • 1
  • Michal Košinár
    • 1
  • Martina Číhalová
    • 1
  1. 1.Department of Computer ScienceVŠB - Technical University of Ostrava Faculty of Electrical Engineering and Computer ScienceOstravaCzech Republic

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